Before executing your computation you might consider visualizing the underlying task graph.
By looking at the inter-connectedness of tasks
you can learn more about potential bottlenecks
where parallelism may not be possile,
or areas where many tasks depend on each other,
which may cause a great deal of communication.

The .visualize method and dask.visualize function work exactly like
the .compute method and dask.compute function,
except that rather than computing the result,
they produce an image of the task graph.

Note that the visualize function is powered by the GraphViz
system library. This library has a few considerations:

You must install both the graphviz system library (with tools like apt-get, yum, or brew)
and the graphviz Python library.
If you use Conda then you need to install python-graphviz,
which will bring along the graphviz system library as a dependency.

Graphviz takes a while on graphs larger than about 100 nodes.
For large computations you might have to simplify your computation a bit
for the visualize method to work well.